# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
    type='EncoderDecoder',
    pretrained='open-mmlab://resnet50_v1c',
    backbone=dict(type='ResNetV1c',
                  depth=50,
                  num_stages=4,
                  out_indices=(0, 1, 2, 3),
                  dilations=(1, 1, 1, 1),
                  strides=(1, 2, 2, 2),
                  norm_cfg=norm_cfg,
                  norm_eval=False,
                  style='pytorch',
                  contract_dilation=True),
    neck=dict(type='FPN',
              in_channels=[256, 512, 1024, 2048],
              out_channels=256,
              num_outs=4),
    decode_head=dict(type='FPNHead',
                     in_channels=[256, 256, 256, 256],
                     in_index=[0, 1, 2, 3],
                     feature_strides=[4, 8, 16, 32],
                     channels=128,
                     dropout_ratio=0.1,
                     num_classes=19,
                     norm_cfg=norm_cfg,
                     align_corners=False,
                     loss_decode=dict(type='CrossEntropyLoss',
                                      use_sigmoid=False,
                                      loss_weight=1.0)),
    # model training and testing settings
    train_cfg=dict(),
    test_cfg=dict(mode='whole'))
